High accuracy particle dimension measurement system

ABSTRACT

A measurement tool connects to an automatic inspection machine for identifying and measuring microscopic dimensions such as area, diameter, height and line width of defects and lines of a photographic mask. An operator draws a rough region of interest around a feature and the tool automatically identifies the feature and calculates its dimensions. For features less than one micron in size, the size of light photons interferes with measurement, so a non-linear polynomial calibration curve is developed for each machine. Features of known sizes are measured on a production machine to produce a calibration curve for each type of defect or line. Features of unknown sizes are measured on the same machine and the measured size in pixels are calibrated using the calibration curve to return a more accurate reading in microns. To determine a dimension, the type of a feature is determined by using a bounding box and light transitions of an intensity profile of the feature; multiple regions of interest are developed for each feature to accommodate angled lines; columns of pixels in each region of interest are summed to produce a light intensity distribution profile for each region of interest; total flux is determined from each profile and the best flux measurement is used to calculate a dimension of the feature. A good image of a feature is obtained by subtracting a reference image from an original image if a profile is of low quality. For lines separated by less than one resolution unit, a full-width half-maximum technique is used to calculate line width.

FIELD OF THE INVENTION

The present invention relates generally to computer measurement systems.More specifically, the present invention relates to the measurement offeatures on photographic masks used in semiconductor manufacturing.

BACKGROUND OF THE INVENTION

The recent introduction of advanced sub-micron sized semiconductordevices require reduced critical dimensions and increased packingdensities. At these sub-micron sizes and high densities, even defectsand imperfections as small as 1 micron and below are problematic andneed to be detected and evaluated. Imperfections in the reticlegenerated by a photographic mask manufacturing process are one source ofdefects. Errors generated by such a photomask manufacturing process havebecome an important issue in the manufacture of semiconductor devices atthese sub-micron sizes. Defect inspection techniques for masks aretherefore becoming to play a more important role in mask making andquality assurance.

Thus, it is becoming increasingly important to be able to identify andto correctly size mask defects, line widths, heights of edge defects andother features that are under 1 micron in size. Accurate sizing of thesefeatures allows masks that are below specification to be repaired, andprevents the needless and costly hold up of masks that do meetspecification. However, one of the problems of assessing reticle qualityat these sub-micron levels on an automatic inspection system is that thesize of these features cannot always be accurately, quickly andcost-effectively measured in a production environment.

Although mask makers typically repair most defects found at earlyinspection stages, invariably, defects are found at later inspectionstages (such as after pelliclization of the mask has occurred). Theselate stage defects are sized and classified relative to a defect sizespecification, the size at which device performance is deemed to beeffected.

Currently, defects found by automatic inspection tools are classified inone of the following categories by a human operator: (1) a real defectis a hard or soft defect that exceeds the defect size specification, (2)a sub-specification defect is a random or process-related defect belowspecification that is within a safety margin, and (3) a false defect isa defect detected by the inspection tool with no apparent cause.

Classification of the above types of defects is largely a subjectiveactivity based upon operator skill. However, as defect sizespecifications diminish, the distinction between real andsub-specification defect classification has become increasinglydifficult. For example, as the line width on sub-micron masks approaches1 micron, the ability to measure defect sizes at 0.1 micron and belowbecomes very important. Current production machines have an accuracy of0.1 micron to 0.2 micron, but this is not sufficient.

It has long been known that mask inspection tools are not measurementtools and that the size information provided by these tools has limitedvalue for measurement-based defect classification. Consequently, manymask makers have incorporated measurement aids at the inspection stationor have moved the mask to a more suitable measurement tool in order tomake classification decisions. Measurement aids used at the inspectionstation include calipers, grids, and software based video image markerssuch as gates, scales, grids, boxes and circles. These aids are fairlyrapid, but ultimately require the operator to "eyeball" the boundariesof the defect. This activity is very subjective and can lead to an errorin the measurement of the defect.

For example, particle size is conventionally measured by measuring thedistance between opposite edges of the particle. Once a defect isidentified by an inspection machine, the operator uses a videomicroscope and a television camera to position a cursor on one side ofthe defect and another cursor on the other side of the defect. Theoperator must judge for himself the exact boundaries of the defect andmust place the cursors where he sees fit. At this point, the operatorpushes a button and the software blindly computes the distance betweenthe two cursors in order to supply a rough approximation of the diameterof the defect. This technique has many disadvantages.

Firstly, this measurement technique is operator dependent in that theoperator must manually position the cursors on the boundaries of whatthe operator believes to be the defect. The operator may misjudge thetype of a defect, its boundaries, or may simply misplace a cursor evenif the defect is visible. The software then blindly calculates thedistance between the cursors, without regard for the type of defect, itstrue boundaries, etc. The above technique may be performed with astandard video microscope and has an accuracy of about 0.1 micron, butis completely subject to the operator's skill level and interpretation.This technique is also unable to calculate an area for a defect.

Another difficulty with light measurements of features less than 1micron in size is that the size of photons begins to interfere with themeasurement of these smaller and smaller feature sizes. Currenttechniques do not adequately address the non-linearities associated withsuch measurements.

Alternatively, the mask may be removed from the automatic inspectiontool and relocated on a more precise and repeatable measurement tool.However, this approach involves removing the mask from production,relocating the defect, and is thus impractical in a productionenvironment. This technique is also costly, time-consuming and increasesthe handling risk. For example, an atomic force microscope (AFM) may beused to measure defect sizes; such a microscope is extremely accuratebut is very slow, very expensive and is still subject to operatorinterpretation.

One approach that has been taken that uses calibration of an automaticinspection system in order to size defects is described in"Characterization Of Defect Sizing On An Automatic Inspection Station",D. Stocker, B. Martin and J. Browne, Photomask Technology and Management(1993). One disadvantage with the approach taken in this paper is thatit only provides a technique for measurement of defects of 1.5 micronsand greater. Such sizes of defects would produce a linear relationshipbetween reference sizes and actual measured sizes, and the paper doesnot account for defects less than 1 micron that would produce anon-linear relationship. Also, the technique does not allow forindividual calibration data for particular types of defects.

Therefore, an objective feature measurement tool is desirable for usewith a photomask inspection tool that can provide reliable andrepeatable measurements of defects and other features below 1 micronthat can be accomplished in a fast and highly practical manner in aproduction environment.

SUMMARY OF THE INVENTION

The present invention discloses a measurement tool that provides anobjective, practical and fast method for accurate sizing of maskfeatures found with an automatic inspection tool (such as a videoinspection machine). Diameters of defects and dimensions of otherfeatures can be measured by using gray scale image information providedby the automatic inspection tool. The present invention may be usedwhile the photomask is in-place at the inspection station, and there isno need for the mask to be removed to a different machine formeasurement. Also, an operator is able to quickly outline a generalregion around a feature to be measured without the need for the operatorto judge the size of the feature. The dimension of the feature is thenautomatically identified and measured quickly by the measurement tool ofthe present invention.

Benefits include avoiding repairing masks within specification, andequivalent results whether measured by customer or supplier (whencalibrated with the same reference). Also, marginal defects areaccurately measured and classified as fatal or not, andsub-specification defects are stored and documented with printoutsincluding picture and text. Operator productivity and tool utilizationis improved by rapid measurements taking place at the inspectionstation. Repair quality may also be checked by taking a line widthmeasurement.

The disclosed measurement tool objectively and repeatedly measuresdefects (spots, holes, intrusions, extensions, etc.), line widths at anyangle (transmissive and opaque vertical and horizontal lines), heightsof edge defects and dimensions of a variety of other features fordetermining photomask disposition. The measurement tool operatesautomatically and is not dependent upon operator judgement. Defects from0.1 to 1.5 microns can be measured, repeatably to 0.02 microns andaccurate to 0.05 microns with a typical AFM calibration. Line widthsless than 1 micron can be measured, repeatably to 0.05 microns andaccurate to 0.1 micron with a typical AFM calibration. Measurements froman AFM, Vickers or OSI machine may be simulated by calibrating to theirstandards. Additionally, the measurement tool provides automaticmeasurements in 1 to 5 seconds (including operator actions).

By analyzing a VERIMASK image and correlating measured pixel widths ofthe feature to the known size of the defect or line width in microns,the inspection machine, its optics and electronics, video hardware, andcomputer hardware and software may all be calibrated. There is no needto calculate variances due to individual camera response, ambient light,individual machine characteristics, or other variables. Thus, ameasurement of an actual defect (for example) using the same hardware toreturn a diameter in pixels may be directly compared to a particularcalibration curve for that type of defect in the calibration database inorder to return an extremely accurate width of the defect in microns.

In one embodiment, test features of known sizes (measured from aVERIMASK, for example, a plate with programmed features and standardsizes for each feature, made by DuPont Photomasks Inc., of Round Rock,Tex.) are first measured in order to serve as a calibration gauge forthe measurement tool. Once dimensions in pixels for these features havebeen determined, these measured dimensions for various sizes of featuresare plotted against the true sizes in microns of the features (knownbeforehand). This calibration procedure yields a polynomial curve thatis used for future measurements of features of unknown sizes on the samehardware, in order to calibrate the measurements. In a furtherembodiment, a separate calibration curve is developed for each type ofdefect and for each variety of line width. Separate calibration curvesfor each feature correct for optical characteristics of the system inrelation to a particular feature and likely skews in the referencemeasurements due to individual features.

In another embodiment of the invention, a non-linear calibrationpolynomial curve is produced relating measured sizes of features from aparticular machine to known reference sizes of these features. A lightmeasurement is performed on features of unknown sizes on the samemachine to return a measurement in pixels. This value in pixels may thenbe compared to the above described calibration curve in order to returnan accurate value of the size of the feature in microns. In a furtherembodiment, calibration curves are developed for each type of feature,and light-measured features of a particular type are compared to theirspecific calibration curve. Advantageously, a calibration curve yields amore accurate measurement.

In another embodiment of the invention, the type of a feature isdetermined by first forming a bounding box around the feature. Analysisof whether the box touches a user region of interest yields either anisolated defect or other feature. Analysis of light transitions forother features yields a determination of the types of these otherfeatures. The feature type is determined automatically.

In yet another embodiment, a good quality source image of a feature isfound by subtracting a reference image. A light intensity distributionprofile is first developed for a region surrounding the feature. If theprofile is not of good quality, then a reference image of the feature isobtained and subtracted from the feature image in order to produce agood quality source image.

In another embodiment of the invention, multiple regions of interest areformed surrounding a feature and an intensity profile is developed foreach region of interest. A total light flux measurement is calculatedfor each profile, and one of the light flux measurements is chosen asthe best flux value. A good quality profile is chosen such that thetotal flux measured from the profile is proportional to the area of thedefect. Multiple regions allow for angled lines.

In a further embodiment of the invention, a region of interest surroundsthe feature and a profile for the feature is produced by summing columnsof pixels across the feature site in the region of interest. A baselineintensity value is determined for the profile and is subtracted from theprofile in order to determine the total flux passing through thefeature. Subtraction of a baseline removes background intensities andobviates the need to obtain a reference image.

In yet another embodiment, the height of edge defects may be measuredaccurately. The height of a defect is important to measure especially ifthe defect occurs on an edge, such as a bump on a line or a bite into aline. The height refers to the two-dimensional distance that anextension sticks out from a line, or how far an intrusion bites into aline (as opposed to the three-dimensional thickness of a defect). Themeasurement of height provides an indication of how close the defectextends to adjacent lines in two dimensions. One column of lightintensities one pixel wide in the profile is summed to determine an areaof the feature that corresponds to the height of the feature.

In a further embodiment of the invention for calculating line width, aregion of interest surrounds a line portion and a profile for the lineportion is produced by summing columns of pixels across the line portionin the region of interest. The total flux is then determined for theprofile and is used to find the area of the line portion,. The area ofthis line portion divided by a dimension of the region of interestyields the line width.

In another embodiment, calibration data is developed for a line in orderto calculate a line width. A light measurement is taken of a line widthof known size that is at a distance of less than one resolution unitfrom another line. A full-width half-maximum technique is used to assistin calculating the line width. The measurements are repeated for numberof line widths of different sizes in order to develop calibration datathat relates light-measured values for line widths to the known widthsof the lines. The calibration data may be represented as a non-linearpolynomial curve that can be referenced by future measurements of linewidths of unknown sizes that are less than one resolution unit fromother lines in order to return more accurate results.

In one other embodiment, non-linear calibration data is developed forline widths of lines that are less than one resolution unit from otherlines. An actual measurement of a line width of unknown size that liesless than one resolution unit from another line may reference thecalibration data in order to return a more accurate result. The use ofcalibration data developed using a full-width half-maximum technique isadvantageous for lines less than one resolution unit apart due tonon-linearities associated with measurements at these small dimensions.

In an additional embodiment, an image of a feature is extracted from thevideo output of an automatic inspection tool. The operator draws a veryrough box around the general feature site without needing to gauge thesize of the defect. Pin point accuracy is not needed. The measurementtool automatically picks out the feature from within the region ofinterest identified by the operator, identifies the feature, andcalculate the area, width and height of the feature. These measurementsare completely independent of the size of the region of interestidentified by the operator, thus removing operator judgement from themeasurement.

Thus, by providing an extremely accurate measurement of mask features,the disclosed measurement tool helps to avoid unnecessary mask repairs.Also, operator variability is eliminated, and overall productivity andmask throughput is increased due to the accurate measurements in-placeand documentation produced in seconds. Because the measurements areautomatic, operator training is minimal. Repair quality can also bequickly checked by using line width measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further advantages thereof, may best beunderstood by reference to the following description taken inconjunction with the accompanying drawings in which:

FIG. 1 illustrates a measurement system in accordance with oneembodiment of the present invention.

FIG. 2 is a block diagram of an embodiment of a computer system used inthe measurement system of FIG. 1.

FIGS. 3A through 3E illustrate various features of a photographic maskeach surrounded by a user region of interest.

FIG. 4 is a flowchart for developing calibration data and measuring thedimensions of a feature of a photographic mask according to oneembodiment.

FIG. 5 is a flowchart for the develop calibration data step of FIG. 4.

FIG . 6 is a flowchart for the compute feature area step of FIG. 5.

FIG. 7 is a flowchart for the pre-process video data step of FIG. 6.

FIG. 7A is a flowchart for the determine intensity range step of FIG. 7.

FIG. 7B is a flowchart for the isolate image and determine feature stepof FIG. 7.

FIG. 8 is a flowchart for the determine flux step of FIG. 6.

FIG. 8A is a flowchart for the produce multiple regions step of FIG. 8.

FIG. 8B is a flowchart for the develop profiles step of FIG. 8.

FIG. 8C is a flowchart for the develop profile statistics step of FIG.8.

FIG. 8D is a flowchart for the determine best flux step of FIG. 8.

FIG. 9 is a graph showing a histogram for a particular feature of amask.

FIGS. 10A through 10D show various orientations for system regions ofinterest that surround a particular defect.

FIG. 10E shows two system regions of interest used for developingprofiles of a line width.

FIGS. 11A through 11C illustrate how a system region of interest of aflux source image may be summed in order to create an intensitydistribution profile for a particular feature.

FIGS. 12A through 12D illustrate possible resulting intensity profilesfor the flux source image of FIG. 11A depending upon the orientation ofthe system regions of interest used.

FIG. 13 illustrates in greater detail the intensity distribution profileof FIG. 11A.

FIG. 14 illustrates the development of an intensity profile for a widthof a line that is separated by greater than one resolution unit fromanother line.

FIG. 15 illustrates a full-width half-maximum technique for developingan intensity profile for the width of a line that is at a distance ofless than one resolution unit from another line.

FIGS. 16A through 16D are calibration graphs for particular types ofdefects from which a reference measurement was obtained using an atomicforce microscope (AFM).

FIGS. 17A through 17D are calibration graphs for different types ofdefects from which a reference measurement was obtained using a Vickersmachine.

FIGS. 18A through 18D are example calibration graphs for different typesof line widths as they may appear once a reference measurement isobtained.

FIG. 19 is a block diagram of a typical computer system suitable forimplementing an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment of the present invention, a number of artificiallyproduced standard features of known sizes are each measured using themeasurement tool of the present invention to produce a calibration graphfor each type of feature that plots the measured dimension of thefeature in pixels versus the known size of the feature in microns. Foreach type of feature, a number of features of the same type of differentsizes are analyzed in order to produce a plot fit with a polynomialcurve. These artificial features may be measured in order to determinetheir true size by using a wide variety of techniques. By way ofexample, an atomic force microscope, Vickers machine or other may beused to determine the true reference size of the features.

Once the calibration graphs have been developed for each type offeature, then a particular real feature of unknown size is measuredusing the measurement tool of the present invention. The amount of lightabsorbed by the feature, or transmitted by a hole in a surface thatshould be opaque, is measured. This flux value is used to produce anarea in pixels of the feature which can then be correlated to the actualreference size in microns of the feature by using the polynomial curveof the previously developed calibration graph for that feature type. Inmost cases for features smaller than 1 micron, it can be assumed thatthe feature is effectively circular, thus, the diameter of the featurecan be computed from its area. This assumption can be justified becausedefects smaller than 1 micron will most often appear circular due to thedefraction of light (light photons are approximately 0.5 microns insize). Additionally, such defects are typically nearly circular. Defectsthat are non-circular (and usually larger than 1 micron) can still bemeasured using the "eyeball" method with the aid of a built-in 1 micronreticle reference grid supplied. Additionally, defects that are notquite opaque are treated as if they were opaque.

Turning now to FIG. 1, a feature measurement system 10 in accordancewith one embodiment of the present invention includes a video inspectionmachine 12, a computer system 14 and a printer 16. Video inspectionmachine 12 may be one of a wide variety of automatic inspection toolsthat analyze microscopic particles, lines, dimensions, etc., and outputsa video image of the microscopic features that it is analyzing. By wayof example, machine 12 may be a KLA 2xx or 3xx, or DRS-1, DRS-2automatic inspection tool used for inspecting photographic masks thatare used in the manufacture of semiconductor devices. Machine 12includes a video camera 20 having a lens tube 22 and a lens 24 that isinspecting a medium 26. Medium 26 may be one of a wide variety of mediahaving microscopic features that are suitable for measurement by thepresent invention. By way of example, medium 26 is a glass reticlehaving a chrome pattern upon it forming a mask used in semiconductormanufacturing. Of course, other materials and substrates may be used toform the pattern of the mask. And a wide variety of other media may besuitable for use with present invention. For example, media such as aprinted circuit board, other transparent media, and other types of masksmay have measurements performed upon them using any of the varioustechniques of the present invention.

In one embodiment, a multi-camera option may be used in which two ormore inspection machines of different types provide video data to themeasurement tool. Each machine may use separate calibration data whichis changed automatically when input is switched to originate from thatmachine.

Computer system 14 may be any suitable computer system for embodying themeasurement tool of the present invention. By way of example, computersystem 14 may be a PC computer having hardware 30, a high resolutionmonitor 32, a keyboard 34 and a mouse or track ball 36. Printer 16 isalso connected to computer system 14 for allowing results of featuremeasurements to be printed.

Computer system 14 is connected to machine 12 via cable 38 which may beany suitable cable for transmitting raw video output data from machine12 to computer system 14. In operation, machine 12 transmits via cable38 multiplexed (in time or by position) feature image data and referencedata to computer 14 for analysis and measurement. The reference datareceived from machine 12 is an image of what a particular portion of themask should look like free of defects. This reference data may beretrieved from a mask database or may be obtained by doing a die to diecomparison. Reference data is used when a good quality profile isdifficult to obtain and will be explained in greater detail below withreference to FIG. 7. Thus, machine 12 transmits not only the results ofmeasuring artificially produced standard features for the purpose ofproducing calibration data, but also transmits live video images andreference images for actual features of unknown dimensions that areidentified upon mask 26.

FIG. 2 illustrates in greater detail the computer system 14 of FIG. 1. Awide variety of computer configurations may be used; one alternativeembodiment for a computer system 14 is shown in FIG. 19. Hardware 30includes a CPU 40 connected to a PCI bus 42 and also connected to anysuitable computer bus 44. Video data from machine 12 travels over cable38 to digitizer hardware 46 that converts the video analog signal todigital form. Hardware 46 is preferably high-resolution video capturehardware.

Once the video data has been converted to digital form by digitizer 46the digital data is stored in video ram 48. Also connected to bus 42 isread-only memory (ROM) 50 and random access memory (RAM) 52. Acalibration database 54 is also accessible via bus 42 and may becontained in any suitable memory of the computer. Calibration database54 contains individual points plotted as shown in FIGS. 16, 17 and 18,and also the equations for the polynomial curves that represent thesepoints. The database will be explained in greater detail below withreference to FIG. 4.

Connected to bus 44 are a wide variety of input and output devices. Byway of example, shown are a floppy disk 56, a hard disk 58, a CD-ROM 60,a network connection 62 in the form of an Ethernet connection, and awide variety of other miscellaneous input and output devices 64 thatinclude printer 16, monitor 32, keyboard 34 and track ball 36.

The measurement system of the present invention is suitable foridentifying and measuring a variety of features such as defects and linewidths present on a photographic mask. A wide variety of defects mayappear during the manufacture of the mask. FIGS. 3A through 3Eillustrate examples of types of defects and a line width. Defectsinclude isolated defects such as spots or holes, edge defects such asextensions and intrusions, and a wide variety of other types of defects.Other features that may be measured include the width of a chrome lineor the width of spacing between such lines.

FIG. 3A shows a feature site 70 to be measured. Feature site 70 includesa spot defect 71 surrounded generally by a user region of interest 72. Abounding box 73 bounds spot 71. A spot defect occurs when a particle ofchrome or other contaminant is present by itself in location where itdoes not belong. As will be explained in greater detail below withreference to FIG. 4, when inspection machine 12 identifies a featuresuch as spot 71, the operator is able to enter review mode and toquickly surround spot 71 with a rough user region of interest 72indicating the region that the user wishes to analyze and measure.Advantageously, the operator need only roughly draw a rough user regionof interest around spot 71, and need not judge for himself the exactboundaries of the defect. Bounding box 73 is created by the measurementtool in order to determine the type of feature that the user has chosento measure and will be explained in greater detail below with referenceto FIG. 7B.

FIG. 3B shows a feature site 75 in which a line 78 has a hole defect 76.Hole 76 is surrounded by a user region of interest 77. A hole may occurwhen a section of a chrome line (for example) is lacking a piece ofchrome such that a hole appears. FIG. 3C shows a feature site 80 inwhich a line 83 has an extension edge defect 81. This defect issurrounded by a user region of interest 82. An extension edge defectoccurs when a portion of a line extends, or bulges out away from theline and is convex in shape. By convention, the height 84 of theextension refers to how far the defect extends from line 83. FIG. 3Dshows a feature site 85 in which a line 88 has an intrusion edge defect86. This defect is surrounded by a user region of interest 87. Anintrusion edge defect occurs when a portion of a line is missing alongan edge and has a concave shape. By convention, the height 89 of theintrusion refers to how far the defect intrudes into line 88.

FIG. 3E shows a feature site 90 in which the width of line 91 is desiredto be measured. A user region of interest 92 encompasses the width ofline 91. Line 91 may be an opaque line, a transmissive clear regionbetween lines, or other. As will be explained in greater detail belowwith reference to FIG. 7B, line 91 presents a dark region surrounded oneither side by bright regions 95 and 96. With each of these defects andfeatures, the operator is able to easily and quickly draw a user regionof interest around the feature site to be measured and need not exerciseany judgement regarding the size of the feature.

A wide variety of other types of defects and features such as dots,protrusions, corner defects, bridges, truncations, misplacements,half-tones, etc., as described in "SEMI Standards Programmed DefectMasks and Its Applications for Defect Inspection", by H. Kawahira and Y.Suzuki, SEMI Japan Standards Committee, Mountain View, Calif., may beanalyzed and measured using the invention disclosed herein.

Now having described examples of various of the types of defects andfeatures that may be measured using the present invention, a technique200 for measuring various dimensions of these features is shown in FIG.4. In one embodiment of the invention, an operator uses the inspectionmachine of FIG. 1 to inspect a photomask and identify features. Once afeature is found, the operator is then able to measure in-place adimension of the feature using a connected computer which receives livevideo information from the inspection machine. Thus, analysis andmeasurement of the feature occurs while the mask is in place in theinspection machine and there is no need to remove the mask to anothermachine for measurement of a feature. This technique allows forextremely rapid measurements to be made. Measurements may be made offeatures of known sizes for producing calibration data, or of featuresof unknown sizes in a production environment.

In step 202, calibration data is developed using features of known sizes(such as from a VERIMASK) on the inspection machine that will be used tomeasure the actual defects. The calibration data is used to correct fornon-linearities in the relationship between measured sizes and actualsizes. The operator interacts with the inspection machine to develop thecalibration data preferably using a sequence of steps similar to steps204-208, although any other suitable technique may be used in order todevelop the calibration data. This step will be explained in greaterdetail below with reference to FIGS. 5-15.

Once the calibration data has been obtained for any number of featuretypes, this data is stored in the calibration database 54 of FIG. 2 andmay appear in graph form as shown in any of FIGS. 16-18. These graphfigures will now be explained before returning to step 204 of FIG. 4.

FIGS. 16A-16D and FIGS. 17A-17D show calibration plots for each ofspecific types of defects, while FIGS. 18A-18D show calibration plotsfor specific types of lines. Each of the graphs has for a vertical axisa measured value for the diameter (or width) of the feature in pixels.Each horizontal axis is the true reference diameter (or width) of thefeature in microns as determined from an AFM measurement, a Vickersmeasurement, or other standard. Both the AFM measurement and the Vickersmeasurement are performed on very expensive, slow microscopes that arenot suitable for production purposes but are extremely accurate. Anobjective absolute standard for obtaining reference measurements wouldbe an NIST version of a VERIMASK plate. Other dimensions could berepresented in these calibration plots such as feature area, height,etc.

FIGS. 16A-16D and 17A-17D show respectively calibration curves for thedefects hole, spot, intrusion and extension. FIGS. 16A-16B use areference micron measurement from an AFM machine, while FIGS. 17A-17Duse a reference micron measurement from a Vickers machine. FIGS. 18A-18Dare possible calibration plots for line widths of horizontal opaquelines, horizontal clear lines, vertical opaque lines and vertical clearlines, respectively, and are illustrative of possible results formeasuring line widths using any suitable reference measurement machinesuch as an AFM or Vickers.

For each feature type (hole, spot, etc.), the measurement tool is usedto measure a number of features of different known sizes of that featuretype using a known standard such as a VERIMASK in order to develop datapoints and a calibration curve such as those seen in FIGS. 16, 17 and18. Because measurements of different features yield different resultsdue to the inherent nature of the feature, having individual plots foreach feature is advantageous in that more accurate measurements can bemade for the actual features of that feature type. For example,referring to FIG. 16A, a measured hole defect having a diameter inpixels of twenty has a true size of 2.1 microns. And with reference toFIG. 16B, measurement of a spot defect having a diameter of twentypixels results in a true size of 1.8 microns. Thus, it can be seen thatfor a measured value of twenty pixels for a defect, there is adifference of 0.3 microns in size depending upon whether the defect is ahole or a spot. Thus, development of calibration curves for each defector line width is advantageous in that it results in greater accuracy fora measurement.

Additionally, separate calibration data for individual features can bedeveloped for each objective of an inspection machine. Differentobjectives may have different characteristics affecting featuremeasurement. Also, the presence or absence of the compensation glass inan inspection machine (as required with through-the-pellicle KLAinspection) may cause different measurement results. Separatecalibration data for individual features can also be developed for thesesituations. In general, a calibration plot can be developed for anyunique combination of hardware in an inspection machine in order tocompensate for its characteristics.

Once calibration data has been developed for each type of feature (suchas shown in the graphs of FIGS. 16, 17 and 18), the measurement tool isthen ready to measure the dimensions of actual features. In step 204,the operator detects a feature such as a defect or line width using theinspection machine. In this step the inspection machine scans the maskand identifies a feature. The inspection machine may identify a featureautomatically, or the operator may assist in the identification of thefeature. This inspection of a photomask may occur in a mask shop beforethe mask is shipped out to a customer, or may also occur in a waferfabrication shop when a mask is received. Once a feature is detected,the inspection machine enters review mode and in step 206 the videoimage of the feature site is displayed on the monitor of the computer.

Next, in step 208 the operator draws a region (or user region ofinterest) around the feature to be measured (such as shown in FIG. 3). Awide variety of techniques may be used by the operator to indicate auser region of interest around the feature. By way of example, theoperator may use a mouse, track ball or other input device to drag aregion around the feature. Advantageously, the operator is not requiredto exercise judgment in placing the region exactly around the feature,but is only required to roughly place a region around the general areaof the feature to be measured. Thus, operator judgment does not effectthe outcome of the measurement, because the measurement tool is adaptedto automatically identify the type of feature within the feature siteand measure its dimensions automatically and accurately without furtheroperator intervention.

Once the feature has been surrounded with a user region of interest, instep 210 the video image from the feature site is used to calculate thedesired dimension of the feature. Dimensions of the feature to becalculated may include its area, diameter, width, height, and otherdimensions. In one preferred embodiment, the calculation of a dimensionof an actual feature may take place in the same way as test features ofknown sizes are measured in step 202 in developing the calibration data.This calculation of a dimension of an actual feature may take place asdescribed in FIGS. 6-15.

Once a dimension of a feature has been calculated, the dimension (suchas the diameter) of a feature is adjusted in step 212 using thecalibration data contained in the calibration database (and as shown ingraph form in the examples of FIGS. 16-18). For example, referring nowto FIG. 16A, if the diameter of a hole defect has been measured to befive pixels, then referring to the plot reveals that a diameter of fivepixels is 0.7 microns in width. In this fashion, a measured dimension ofa feature in pixels can be referenced to the calibration data in orderto obtain an extremely accurate "true" value for the dimension of thefeature in microns. This technique can be used for any dimensions suchas area, diameter, width, height, etc. The creation of this calibrationdatabase which is represented graphically in the example plots of FIGS.16, 17 and 18 will be explained in greater detail below with referenceto FIG. 5.

Once the dimension (such as diameter, width, height, etc.) of thefeature has been accurately determined, in step 214 the dimension of thefeature in microns is displayed on the computer monitor. In step 216 thefeature image is magnified and displayed with a one by one micron gridfor a sanity check. In step 218, the operator has the option to storethe feature and its measured dimensions along with a description into afeature database for later retrieval and analysis. The operator may alsoprint images of the feature and its dimensions at this time. It shouldbe noted that at any time in this process, previously stored featuresand dimensions may be reviewed, and these selected features andassociated text can be selected and printed. Once the operator hasfinished with a particular feature, in step 220 the live image displayis returned to the computer monitor and the operator continues to detectand measure additional features in step 204.

FIG. 5 illustrates one embodiment of a technique for performing thedevelop calibration data step 202 of FIG. 4. This step is used todevelop calibration data for each of a variety of feature types such asdefects and lines. For each type of defect and/or lines, a number ofdifferent sizes for that particular defect or line width will bemeasured in order to produce a number of data points for producing apolynomial curve. A polynomial curve typically results from the plottingof data points for features less than 1 micron in size because thesesizes are approaching the size of the light photons used to measure thefeature. In other words, the relationship is non-linear and a polynomialcurve results (often quadratic) because the features are so small thatthe size of the light photons (about 0.5 microns) used to measure thefeatures starts to interfere with the measurement. A polynomial curve isadvantageous when the size of features to be measured is less than twicethe wavelength of the light used to illuminate and measure the feature.By way of example, for visible light, a polynomial curve is advantageousfor features less than one micron in diameter.

For example, referring to the hole defect plot of FIG. 16A, it can beseen that seven different sizes of hole defects ranging from about 0.5microns to 1.1 microns have been measured in order to develop anon-linear calibration curve for that type of defect. Thus, a measuredvalue for an actual hole defect may reference this data to obtain a moreaccurate measurement.

Step 230 implements a loop through each type of feature and developscalibration data to produce a calibration plot for each feature type. Inone embodiment, there are eight feature types that include the defectshole, spot, intrusion and extension, and line width features thatinclude horizontal opaque lines, horizontal clear lines, vertical opaquelines, and vertical clear lines. Of course, calibration plots may bedeveloped for other feature types. When all features have been analyzedand measured this step is done. Step 232 implements a loop that measuresa variety of data points for each type of feature. For example, as shownin the hole defect plot of FIG. 16A, there are seven sizes of holes thatare measured that produce seven data points for the plot resulting in aparticular calibration curve for that type of defect. Any number ofsizes of a feature type may be measured in this step. For each size ofthese artificial features, steps 234 and 236 are executed. Once each ofthe sizes has been measured, control moves to step 238.

In step 234 the feature area and height for a particular size of aparticular feature type is computed. This step will be explained ingreater detail below with reference to FIG. 6. Also, the line width maybe calculated from the feature area because as seen in FIG. 10E, area395 from profile 396 of line 392 when divided by the region of interest(ROI) height yields line width 394. Additionally, this step may also beused to calculate dimensions of an actual feature to be measured such asdescribed in step 210 of FIG. 4.

In step 236 the computed feature area for the particular size of aparticular feature is added to the calibration database along with thepreviously (or known) feature size. In this step, the measured height ofthe feature may also be added to the calibration database.

The calibration database may be implemented and organized in a widevariety of manners. By way of example, the calibration database containsa list of the measured feature area (or other dimension) for each defectsize and its corresponding reference size in microns. The reference sizein microns is known because the VERIMASK has artificially produceddefects and line widths with known sizes that have been measured usingan extremely accurate measurement device such as an atomic forcemicroscope (AFM) or a Vickers machine. In step 236 when the measuredfeature area is added to the calibration database, the operator may beprompted to enter the reference size in microns, or the reference sizein microns may be entered automatically along with the measured size inpixels by the computer. In one embodiment, a defect is assumed to becircular, the diameter of the defect is determined from the measuredfeature area and this diameter in pixels is entered into the calibrationdatabase along with its corresponding true diameter in referencemicrons. Such an entry in the calibration database represents one of thedata points such as those shown in FIG. 16A or any of the other plotsshown in FIGS. 16, 17 or 18. Once step 232 adds the data associated withone feature size, control returns to step 232.

Once step 232 has finished computing all of the data points for aparticular feature type, control moves to step 238. In step 238, thedata points developed for a particular feature type are used to producea calibration curve such as those seen in FIGS. 16, 17 and 18. Thiscalibration curve and its corresponding polynomial formula are then alsostored within the calibration database for later reference indetermining the actual size of an actual defect. In one embodiment ofthe invention, step 238 is performed by computing a least square curvefor each feature type (including the data point 0,0). This curve and itscorresponding polynomial formula (such as ax² +bx +c) then stored in thecalibration database. Once a calibration curve has been computed andstored in the calibration database for a particular type of feature,control returns to step 230 and calibration curves are developed foreach of the remaining types of features. Once all of the calibrationdata has been obtained for each feature type, then the developcalibration data step is done.

FIG. 6 illustrates one embodiment of a technique for computing thefeature area and height, step 234 of FIG. 5. In step 250, the video datais pre-processed in order to determine an intensity range, determine thefeature type, determine a size for system regions of interest around thefeature and to produce a good quality flux source image from which theflux passing through the feature will be determined. The video databeing processed may represent test data for a standard feature of aknown size used for developing calibration data, or may represent videodata of an actual feature to be measured. This step will be explained ingreater detail below with reference to FIG. 7.

Once the video data has been pre-processed and a good quality fluxsource image produced, in step 252 the flux that is passed or blocked bythe feature is determined. This step develops multiple profiles of aparticular feature and chooses the best profile in order to determinethe flux. Flux corresponds to the number of light photons passingthrough a medium and is expressed in units of scaled photons. An opaquefeature such as a spot or a line blocks the passage of photons andreduces flux, while a clear portion of a mask such as formed by thespace between lines, or the gap created by an intrusion into a line,passes photons easily and results in an increase in flux. This step willbe explained in greater detail below with reference to FIG. 8.

Once the flux has been determined, in step 254 the number of pixels ofthe feature area is determined by dividing the determined flux by theintensity range. In this fashion, the feature area in pixels can bedetermined and the diameter of the defect or the width of the line canbe easily determined from the feature area. For example, the line widthdimension may be calculated from the feature area because as seen inFIG. 11E, area 395 from profile 396 of line 392 when divided by theregion of interest (ROI) height yields line width 394. Because flux ismeasured in photons, and the intensity range is measured inphotons/square pixel, the division of flux by intensity range gives anarea in square pixels which yields a diameter or width. This measureddiameter or width in pixels can then be added to the calibrationdatabase along with the reference feature dimension (if calibration datais being developed), or the diameter or width in pixels can bereferenced to one of the calibration plots in order to return a truesize in microns (if an actual defect or line width is being measured).

In step 256 the total flux determined from one column of the intensityprofile can be used to determine the height of a defect or line bydividing the total flux by the intensity range. This height in pixelscan then be added to the calibration database (for test features) or maybe referenced to a calibration plot for determining an accurate heightof actual features. Determining height is advantageous for evaluatinghow far edge defects extend from, or intrude into a line.

FIG. 7 illustrates an embodiment of the pre-process video data step ofFIG. 6. This step is used to determine the intensity range for thefeature, determine the type of the feature, to obtain a reference imageif needed, and to produce a good quality flux source image. In step 260the camera gamma value is used to correct for the gamma offset of thecamera optics. A camera gamma value is intrinsic to all cameraelectronics and has a known value for each camera. Because the lightinput to the camera versus the voltage output is non-linear, it iscorrected for in this step.

In step 262 the intensity range for the feature to be measured isdetermined. An intensity value or range may be determined in a widevariety of manners. By way of example, step 262 presents one possibletechnique. The value for intensity range represents a range from thedark peak mean to the bright peak mean and is constant for changes inillumination or camera gain. Intensity range is expressed in digitizedunits (representing photons/square pixel), ranging typically from 0 to255 for a gray scale image. A "0" value represents one end of thespectrum such as chrome being present, and a "255" value represents theother end such as a clear portion of the mask. Generally, a value of 255for intensity is equivalent to about 10,000 photons. This step will beexplained in greater detail below with reference to FIG. 7A.

In step 264 the feature to be measured is isolated and its type isdetermined. A bounding box is used to surround the feature and todetermine its type. This step will be explained in greater detail belowwith reference to FIG. 7B.

The following steps 266-272 ensure that a good quality flux source imageis available for producing a good profile. The development and use ofprofiles in determining flux will be explained in greater detail belowwith reference to FIG. 8. Actual features to be measured often end upproducing a low quality profile having a poor baseline (jagged, uneven,or non-linear) because the feature may be fairly complex. For example, adefect such as an edge defect may be located on a curve of a line ratherthan on a straight edge, and isolated defects may be located extremelyclose to other features which effect the developed profile for thatfeature. Thus, for actual features measured, if a good profile can notbe obtained, it may be necessary to obtain a reference image for thatfeature. FIG. 13 is an example of a profile having a good baseline,i.e., the baseline is fairly linear and the standard deviations of itsleft and right margins are fairly low.

If a profile has been developed for an actual feature (as will bediscussed in FIG. 8), and that profile has a baseline that is notstraight, then steps 270 and 272 are performed in order to obtain a goodquality flux source image. In step 270 the operator is prompted toobtain the reference image for the feature site under consideration andthis reference image is then subtracted from the current actual featuresite which contains the defect. A reference image is a good image of themask as it should appear without a defect. This reference image may beobtained from a mask database, or from a mask on a previous die. Bysubtracting the reference image from the actual image, any complexfeatures surrounding the feature to be measured are removed from theimage and only the defect to be measured remains in the image. Next, instep 272 this difference image is stored as the flux source image.

On the other hand, artificial defects and most of the actual featuresare treated differently. Because a standard, artificial defect used forcalibration purposes is very simple and not complex, such a featureusually always has a good profile with a straight baseline. Thus, forcalibration features, the result of step 266 is no, and in step 268 thecurrent video image is stored as the flux source image. Also, for themeasurement of actual features, the first time the feature is measuredthere is no profile developed at this point in time so the video imagefor that feature will also be stored as the flux source image in step268. Additionally, for measurement of line widths, a good profile isusually obtained so step 268 is performed. Once a video image has beenstored as the flux source image (in either steps 268 or 272), themeasurement tool is ready to determine the flux passed or blocked by thefeature by referring to the flux source image.

FIG. 7A illustrates one technique for determining the intensity range,step 262 of FIG. 7. The intensity range is used along with the fluxdetermined in order to determine the number of pixels in the featurearea. A value for the intensity range is used instead of just anintensity value to compensate for light changes affecting the fluxvalue. For example, if the camera gain doubles or more illumination isused on a feature then the flux would double and affect the measurementof the feature dimension. But if the illumination doubles, the intensityrange would also double and a measurement would stay constant.

In step 274 an intensity histogram for the feature is computed. Ahistogram is a plot of light intensity values versus areas occupied byeach intensity value and is used in order to determine the intensityrange for the feature site. FIG. 9 illustrates an example of a histogram300 that may be used to determine the intensity range of a particularfeature. This histogram example has an intensity axis 302 ranging from 0to 255 and an area axis 304 representing the area in pixels that aparticular intensity occupies. This example feature as a histogram 306defining dark intensity areas 308, bright intensity areas 310, andintermediate gray intensity areas 312. The top of the dark area yields adark peak mean 314 and the top of the bright area yields a bright peakmean at 316. Using the computed histogram for this feature, in step 276the dark peak mean intensity and the bright peak mean intensity aredetermined by reference to the dark peak mean 314 and the bright peakmean 316 of the computed histogram. The intensity range is thendetermined by subtracting the dark peak mean intensity from the brightpeak mean intensity. Thus, were illumination to double, the peaks wouldbe twice as far apart and the intensity range would also double and ameasured feature area would remain constant.

Step 278 determines whether a good histogram has been obtained bydetermining whether one of the peaks is missing. A good histogram willtypically have a dark peak that is four times greater in area than thevalley of the gray region 312 and a bright peak that is four timesgreater in area than the valley region. If a good histogram cannot beobtained by analyzing a small region around the defect, then a largerregion around the defect is analyzed. For isolated defects, such asspots or holes, there will either be all black or all white around thedefect, so it makes no difference how big the analyzed image is. If agood histogram cannot be obtained by analyzing a larger region aroundthe defect, then in step 280 the histogram value from the best recentgood image is used.

FIG. 7B illustrates a technique for isolating the image of the featureto be measured and for determining its type, step 264 of FIG. 7. Thisstep returns a size for the system regions of interest used indeveloping profiles of FIG. 8 and also determines the type of thefeature. In step 282, bounding boxes are determined for the dark andbright portions within the video image of the user region of interest. Abounding box determines the extent of contiguous dark or brightfeatures. Determining bounding boxes may be done in a wide variety ofmanners. By way of example, the technique known as "blob analysis" maybe used to determine the bounding boxes.

For example, feature site 70 of FIG. 3A shows a spot defect 71surrounded by a bounding box 73. This bounding box 73 tightly conformsto the outline of spot 71 and is completely contained within user regionof interest 72. A bounding box for hole 76 of FIG. 3B would similarlytightly surround the hole and would be contained completely within theuser region of interest. By contrast, bounding boxes formed for otherfeatures such as the extension, intrusion and line of FIGS. 3C, 3D and3E would not be completely contained with the user region of interestbut would be collinear with the identified user region of interest.Bounding boxes for these features touch the user region of interestbecause the dark and bright areas of the feature are not completelyisolated within the user region of interest but extend to the border ofthe user region of interest.

In this way, an analysis of the bounding boxes formed can help determinethe type of the feature. In step 284 it is determined if the boundingbox for a dark or bright feature touches the edge of the user region ofinterest. If the answer is no, then in step 286 it is determined thatthe feature is an isolated defect such as a spot or a hole, and the sizeused to help determine the size of system regions of interest isdetermined to be the size of the bounding box.

However, if the bounding box does touch the user region of interest,then the feature may be an edge defect or a line width. Thus, in step288 an intensity profile is taken around the perimeter of the userregion of interest. Because the user region of interest may beasymmetrical, it is advantageous to take the profile around theperimeter, although in an ideal situation a portion of the perimeter maybe used. This intensity profile will identify dark and bright areas andtransitions in-between. In step 290 the number of dark to bright (orbright to dark) transitions in the intensity profile are counted. Anexample of the use of counting such dark to bright transitions may beseen in the examples of FIGS. 3D and 3E. For example, in FIG. 3D theedge defect within the user region of interest has only one bright areaoutside the line and the one dark area being the line itself. Therefore,there is only one dark to bright transition from the line to the areaoutside the line. By contrast, FIG. 3E shows how bright regions 95 and96 are outside of the dark line region 91. Thus, there are two dark tobright transitions, one on each side of line 91.

The number of dark to bright transitions may then be used to determinethe type of feature. Step 292 tests whether there is one dark to brighttransition. If so, then in step 294 it is determined that the feature isan edge defect and the size used to help determine the size of systemregions of interest is determined to be the complete user region ofinterest. However, if there is more than one dark to bright transition,then step 296 determines that the feature is not a defect but is a linewidth, and the size used to help determine the size of system regions ofinterest is determined to be the user region of interest. After steps286, 294 and 296 have completed, step 264 of FIG. 7 is done.

Once the pre-processing of the video data has determined the intensityrange for the feature, has determined the type of the feature and hasreturned a size for system regions of interest, FIG. 8 provides atechnique by which the flux that is passed or blocked by the feature inthe video image is determined, step 252 of FIG. 6. Once the flux isdetermined, then the area of the feature may be calculated. FIG. 8illustrates a technique by which multiple regions of interest (systemregions, as opposed to the original user region of interest) are eachused to create a profile for the feature to be measured. These profilesare then analyzed to determine which profile provides the best fluxmeasurement for the feature to be measured. Through the use of thistechnique, edge defects that lie along an edge angled off of thehorizontal may be measured accurately, as well as line widths for linesthat are not horizontal.

In step 400 multiple system regions of interest for a particular featureto be measured are produced depending upon whether the feature is adefect or a line width. These multiple regions of interest areadditional regions of interest developed by the measurement tool and aredistinct from the original user region of interest that the operatorspecifies in identifying a feature. This step will be discussed ingreater detail below with reference to FIG. 8A. Next, in step 402 aprofile is developed for each of the regions of interest produced instep 400 and is discussed in greater detail below with reference to FIG.8B. In step 404 profile statistics are developed for each profile inorder to determine which profile provides the best flux measurement andis discussed in greater detail below with reference to FIG. 8C. In step406 the profile statistics for each of the profiles is used to determinewhich profile provides the best flux measurement for the feature ofinterest, and is discussed in greater detail below with reference toFIG. 8D. The best flux measurement is then used as the determined fluxvalue for step 252 of FIG. 6.

FIG. 8A illustrates one embodiment of a technique for producing multipleregions of interest, step 400 of FIG. 8. Step 410 tests whether thefeature is a line width or a defect, the type of feature already havingbeen determined above in FIG. 7B. If the feature is an isolated or edgedefect, then in step 412 four system regions of interest are developedfor the defect.

Examples of four possible system regions of interest developed for aspot defect (for example) are shown in FIGS. 10A-10D. FIG. 10A shows avertical region of interest 350 surrounding a spot defect 352. FIG. 10Bshows a horizontal region of interest 360 surrounding a spot defect 362,and FIGS. 10C and 10D show angled regions of interest 370 and 380surrounding the spot defects 372 and 382 respectively. Of course, manyother orientations for the multiple regions are possible.

Multiple regions of interest are useful for producing a good profile ofthe defect. If the defect is on an edge, then a profile is best takenparallel to the edge in order to obtain a good profile with a straightbaseline. Also, if the defect is near or on a diagonal line, then theprofile should be taken on the diagonal that is parallel to the diagonalline. And because these edges or lines may be horizontal, vertical, orat a 45 degree angle, multiple regions of interest that run parallel tothese edges are useful. Most lines and edges on a photomask arehorizontal or vertical, but some are at a 45 degree angle.

Because of feature crowding on a photomask (due to the ever decreasingsize of the mask and its features), isolated defects may also often befound on or near a horizontal, vertical or diagonal line. Also, it islikely that a defect may be found on an edge. Thus, the development ofmultiple regions of interest ensure that at least one of the multipleregions of interest will enable a profile to be taken parallel to anedge near the defect.

Height 391 and width 393 conventions for system regions of interest areshown in FIG. 10E. The size and angle of each of the system regions ofinterest developed in step 412 for a defect may be determined in a widevariety of manners. By way of example, the width may be determined bymultiplying the Blur Size by four, and the height may be determined byadding the Blur Size to a user defined height or to the size determinedin FIG. 7B. The user defined height can be pre-programmed or chosen bythe user from the computer. The angle for the regions of interest inFIGS. 10C and 10D may be any angle; a 45 degree angle works well.

The Blur Size is an empirically determined value in pixels that is anestimate of the resolution unit for the optics used; calculation of itsvalue will be explained in greater detail below with reference to FIG.13. It should be appreciated that any number of regions of interest maybe developed, although developing fewer regions of interest may resultin a poor profile being developed, leading to an inadequate measurementof the feature area. Once these multiple regions of interest have beendeveloped in step 412, then step 400 is done.

Returning now to step 410, if the feature is determined to be a linewidth, then in step 414 two regions of interest are developed along theline to be measured. Examples of two regions of interest 396 and 398 areshown in FIG. 10E. These two regions of interest are developed alongline 392 having a width 394. Two regions of interest are used to helpdetermine the flux through a line because the line may be slightlyangled. By providing two parallel regions of interest 396 and 398, theangle of the line can be corrected for as will be explained in greaterdetail below with reference to FIG. 8D. The height 391 and width 393 ofthese two regions of interest may be any suitable value. By way ofexample, a width equal to a user defined height (or the size determinedin FIG. 7B) plus twice the Blur Size works well. A height equal to sevenpixels also works well. Once these two regions have been developed instep 414 then step 400 is done.

After the multiple regions of interest are produced, then in FIG. 8B atechnique is shown for developing an intensity distribution profile foreach of the produced system regions of interest. This technique will beexplained with reference to FIGS. 11 and 12. Step 420 of FIG. 8B loopsthrough steps 422-426 for each of the multiple regions of interestproduced in step 400 of FIG. 8. Once a profile has been developed foreach region of interest then step 402 is done.

In step 422 one of the multiple regions of interest is divided into anumber of lines in the direction of the region of interest in order toform rows of pixels. This step is illustrated in FIG. 11A, for example.FIG. 11A shows a process 500 in which a region of interest 502surrounding a spot defect 504 is used to develop a profile 506. Becauseregion of interest 502 derives from a video image of the feature site,it is composed of pixels. This step divides the region of interest intorows of pixels 508 along the direction of the region of interest, whichin this case happens to be horizontal. A region of interest that wasangled would be divided into rows of pixels that are parallel to itslength.

Next, in step 424 corresponding pixels in the rows of pixels 508 aresummed in each column in order to form an intensity distribution profile506. As shown in FIG. 11A, a column of pixels 510 is summed in order toform a portion of the profile 506. Using this technique, profile 506 hasa flat baseline 512 except in the location of the spot defect 504 whichis where a dip in the intensity of the profile 514 is caused by spotdefect 504. The intensity dips at this point because spot defect 504prevents flux from passing through the spot.

Examples of developed profiles for other types of defects are shown inFIGS. 11B and 11C. FIG. 11B shows a process 510 in which a region ofinterest 512 has an intrusion defect 514 into a line 516. Summingcolumns of pixels for this region of interest produces a profile 518.This profile also has a flat baseline 520 because the pixels are summedin columns perpendicular to the edge of line 516. The increase inintensity for profile 518 at 522 is caused by intrusion defect 514 whichallows more flux to pass through, thus creating a greater intensity oflight at defect 514 which creates a higher intensity 522 in profile 518.

FIG. 11C shows a process 520 in which a region of interest 522 has anextension defect 524 on a line 526. Summing columns of pixels for thisregion results in a profile 528 having a lower intensity in region 529due to defect 524. It should be appreciated that developing a profilefor other defects such as a hole defect, and for other features such asline widths may be performed in a similar manner.

FIGS. 12A-12D illustrate examples of profiles that may be developed fora spot defect using the regions of interest shown in FIGS. 10A-10D,respectively. FIGS. 12A-12D show profiles 530, 540, 550 and 560. thathave widely varying intensity distribution profiles for theircorresponding regions of interest. Only profile 540 of FIG. 12B has ahigh quality profile in which the baseline 541 is flat and the featureregion 542 of the profile will be proportional to the area of thefeature. These varying profile results illustrate how multiple regionsof interest can be used to find a high quality profile. Certain regionsof interest may produce a poor profile, while others may produce a goodprofile. Thus, the use of multiple regions is advantageous. Thedevelopment of statistics for each profile in order to find the bestprofile will be explained below with reference to FIGS. 8C and 8D.

Once an intensity distribution profile has been formed for a region ofinterest, in step 426 this profile is added to a profile list for laterreference. Control then returns to step 420 and other profiles aredeveloped for the remaining regions of interest. Once profiles have beendeveloped for each of these regions of interest, the quality of theprofiles can be evaluated by developing profile statistics.

Development of profile statistics will now be explained with referenceto FIG. 8C and FIG. 13. FIG. 8C is a flowchart illustrating a techniquefor the development of profile statistics. FIG. 13 shows in greaterdetail an intensity distribution profile 600, similar to either of theprofiles of FIG. 11A or FIG. 12B. Profile 600 has been formed from aparticular region of interest surrounding a spot defect or extensiondefect. It should be appreciated that profile 600 is an example of aprofile and that various feature types may result in profiles ofdifferent forms. Profile 600 is plotted on a graph having an intensityaxis 602 and a distance axis 604. Profile 600 has a feature region 606containing the summed intensities of the pixels for particular columnswithin the feature to be measured. Profile 600 also has a baseline 608and left and right margin portions 610 and 612.

Returning now to FIG. 8C, a technique for developing profile statisticswill now be described. Step 430 loops through each of the profilesdeveloped for each region of interest. Once statistics have beendeveloped for all profiles then step 404 is done.

In developing profile statistics the use of a unit of resolution for theoptics being used is helpful. In one embodiment of the invention, a BlurSize is determined empirically to estimate the resolution unit. In oneembodiment, the Blur Size may be empirically determined as follows.Using a good quality profile having a fairly horizontal baseline,measurements are taken at 20% of the maximum intensity 630 and at 80% ofthe maximum intensity 634. The number of pixels located horizontallywithin the range bounded by the 20% and the 80% values is determined tobe the Blur Size 636 which is an estimate of the resolution unit. Thisis a valid approximation of a resolution unit because were the optics tobe perfect and everything to be in sharp focus, the intensity would notdrop off gradually from maximum to zero but would transitionimmediately. Thus, since blurring causes a gradual drop off ofintensity, a measurement of a Blur Size from 20% to 80% of intensity isa reasonable estimation of the resolution unit. Of course, other valuesand techniques may also be used in order to determine a resolution unit.

In step 432 the left edge 620 and the right edge 622 of profile 600 aredetermined by finding these corresponding points at the one-half maximumintensity level 624. Next, in step 434 a distance equal to oneresolution unit 640 and 642 is measured from the left edge 620 and fromthe right edge 622 respectively, in order to determine an edge of theleft margin 644 and an edge of the right margin 646. The correspondingouter edges 648 and 650 of the left and right margins respectively, aredetermined by finding points 652 and 654 at 5% of the maximum intensity626, respectively, of profile 600.

Once the left margin portion 610 and the right margin portion 612 havebeen defined by locating their respective edges, the average of theintensity values along these two margins is used to form a baselineintensity 608 in step 436. Next, in step 438 baseline intensity 608 issubtracted from profile 600 in order to leave only feature region 606.Subtracting the baseline has a similar effect as step 270 of FIG. 7, inthat background material is subtracted out. That is, the intensityvalues not due to the presence of a feature are removed. The advantageof subtracting a baseline is that no reference image need be obtained.More specifically, subtraction of a baseline accommodates for unevenillumination or slight rotation between the lines on the mask and theregion of interest. These cause the baseline to be tilted but straight.After baseline subtraction, the margins yield a low standard deviationas described in the following paragraph.

In step 440, the standard deviations for the left and right marginintensities are determined in order to give an indication as to how flatthe baseline is, which in turn indicates whether the profile is of goodquality and would return an accurate flux reading. For example, a lowstandard deviation indicates a good baseline, while excess noise in thedata would cause data points to have a larger deviation from the meanand result in a poorer baseline. In step 442 the intensities betweenedge 644 of the left margin and edge 646 of the right margin are summedin order to compute the total flux that passes through the feature. Thistotal flux can then be used to determine the area of the feature orother dimensions.

The total flux is also computed to assist in determining the height ofthe feature or its line width. For line width, the total flux yields thearea for the region of interest, which corresponds an area of a portionof the line to be measured. For example, FIG. 10E shows an area 395 ofline 392. If the total flux is determined, this yields the value forarea 395. Dividing this area value by the ROI height 391 yields the linewidth 394.

Height of a feature can also be determined from the total flux of onecolumn of the profile. Dividing the total flux by the intensity rangeyields the area of that column. If an assumption is made that the defectis opaque (or totally clear), then the area of that column leads to aheight measurement because the column is one pixel wide. Thus, theheight is equivalent to the area.

Once the flux and the standard deviation have been computed for theprofile, this data for the profile is stored for later reference in step444. Control then returns to step 430 in which statistics are developedfor any remaining profiles.

FIG. 8D illustrates an embodiment of the determine best flux step ofFIG. 8. The best flux measurement will be determined by reference to theprofile statistics developed for each profile in FIG. 8C. Step 450determines whether the feature is a defect or line width. If the featureis a defect, then the profile with the lowest average standard deviationfor the left and right margins is picked and the total flux measurementassociated with that profile is returned to FIG. 6 as the determinedflux for the defect. Only one of the profiles is picked in step 452because only the profile with the lowest standard deviation for themargins will have the best and most accurate flux measurement for thedefect. That is, only the best profile will have a total fluxmeasurement that is proportional to the area of the defect.

As can be seen with reference to FIGS. 12A through 12D, variousorientations for regions of interest produce wildly varying profiles.Since profile 540 of FIG. 12B (for example) has a relatively flatbaseline 541, the average standard deviation for its left and rightmargins will be extremely low, indicating that this profile provides afeature region 542 having a flux measurement that most accuratelyreflects the flux passing through or being blocked by the defect. Oncethis profile and flux are determined in step 452, step 406 is done.

On the other hand, if the feature is a line width, then in step 454 theaverage of the total flux values for all profiles is determined. In step454 the average of the total flux values is determined instead ofpicking a profile with the lowest standard deviation because the tworegions of interest produced for a line width (as shown in FIG. 10E) areboth at the same angle to the line and will produce nearly similarprofiles and total flux values. In step 456 a rotation correction iscomputed from the left and right edge positions of both profiles inorder to compensate for a line that may be angled. This rotationcorrection may be performed in a wide variety of manners. By way ofexample, the following formulas may be used:

    theta=fabs(atan*2(Measured Center Difference, ROI Spacing Distance))

    theta correction=cosine(theta)

where Measured Center Difference is the difference between the left handedge positions of the profiles for each region of interest and ROISpacing Distance is the distance in pixels between the two regions ofinterest.

Next, in step 458 the computed rotation correction (theta correction) ismultiplied by the average of the total flux values in order to determinethe correct flux measurement for the measured line width. Step 406 isthen done.

In certain circumstances another technique may be used to develop aprofile for determining a line width. This technique will now bediscussed with reference to FIGS. 14 and 15. FIG. 14 shows a process 700of developing a profile 702 for a line 704 that is greater than adistance 706 of one resolution unit from another line 708. As long asthe two lines 704 and 708 are separated by a distance greater than oneresolution unit, then summation of the pixels within the line region 704results in a good profile 702 having a feature region 708 whichcorresponds to the width of line 704.

By contrast, FIG. 15 illustrates a process 750 of developing a profile752 for a line 754 which is at a distance 756 from line 758 that is lessthan one resolution unit. In this example, because the two lines areseparated by less than one resolution unit, profile 752 results inhaving a feature region 760, a feature region 762 and an intermediateregion 764 which together do not correspond to the width of line 754.This enlarged feature region formed by regions 760, 762 and 764 occursbecause the two lines are closer than one resolution unit apart. Such acombined region is not useful for developing a good baseline withoutfurther processing.

However, by using a full-width half-maximum technique, a feature region762 may still be defined that does correspond to the width of line 754.In this technique, points 770 and 772 at 50% of the intensity of theprofile are defined that then correspond to the left and right edges ofthe appropriate feature region. Once this feature region has beendefined, feature region 762 may then be analyzed as described above withreference to FIGS. 8C, 8D and 13 in order to develop profile statisticsand produce a flux measurement that is indicative of the true width ofline 754. If such a technique is used, then four additional calibrationplots would be used in order to provide calibration data for each of thefour types of line widths that are measured using the full-widthhalf-maximum technique.

Development of separate calibration plots for use in measuring linewidths that are closer than one resolution unit to other lines orfeatures is advantageous because of non-linearities associated withthese types of measurements. Non-linearities occur in part because thecloseness of other features causes measurement of the line in questionto be distorted. Development of a polynomial calibration curve formeasuring these types of line widths results in a much more accuratedetermination of the line width.

COMPUTER SYSTEM EMBODIMENT

Embodiments of the present invention as described above employs variousprocess steps involving data stored in computer systems. These steps arethose requiring physical manipulation of physical quantities. Usually,though not necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. It is sometimes convenient,principally for reasons of common usage, to refer to these signals asbits, values, elements, variables, characters, data structures, or thelike. It should be remembered, however, that all of these and similarterms are to be associated with the appropriate physical quantities andare merely convenient labels applied to these quantities.

Further, the manipulations performed are often referred to in terms suchas identifying, running, or comparing. In any of the operationsdescribed herein that form part of the present invention theseoperations are machine operations. Useful machines for performing theoperations of embodiments of the present invention include generalpurpose digital computers or other similar devices. In all cases, thereshould be borne in mind the distinction between the method of operationsin operating a computer and the method of computation itself.Embodiments of the present invention relate to method steps foroperating a computer in processing electrical or other physical signalsto generate other desired physical signals.

Embodiments of the present invention also relate to an apparatus forperforming these operations. This apparatus may be specially constructedfor the required purposes, or it may be a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. The processes presented herein are not inherently relatedto any particular computer or other apparatus. In particular, variousgeneral purpose machines may be used with programs written in accordancewith the teachings herein, or it may be more convenient to construct amore specialized apparatus to perform the required method steps. Therequired structure for a variety of these machines will appear from thedescription given above.

In addition, embodiments of the present invention further relate tocomputer readable media that include program instructions for performingvarious computer-implemented operations. The media and programinstructions may be those specially designed and constructed for thepurposes of the present invention, or they may be of the kind well knownand available to those having skill in the computer software arts.Examples of computer-readable media include, but are not limited to,magnetic media such as hard disks, floppy disks, and magnetic tape;optical media such as CD-ROM disks; magneto-optical media such asfloptical disks; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory devices(ROM) and random access memory (RAM). Examples of program instructionsinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter.

FIG. 19 illustrates a typical computer system in accordance with anembodiment of the present invention. The computer system 100 includesany number of processors 102 (also referred to as central processingunits, or CPUs) that are coupled to storage devices including primarystorage 106 (typically a random access memory, or RAM), primary storage104 (typically a read only memory, or ROM). As is well known in the art,primary storage 104 acts to transfer data and instructionsuni-directionally to the CPU and primary storage 106 is used typicallyto transfer data and instructions in a bi-directional manner. Both ofthese primary storage devices may include any suitable of thecomputer-readable media described above. A mass storage device 108 isalso coupled bi-directionally to CPU 102 and provides additional datastorage capacity and may include any of the computer-readable mediadescribed above. The mass storage device 108 may be used to storeprograms, data and the like and is typically a secondary storage mediumsuch as a hard disk that is slower than primary storage. It will beappreciated that the information retained within the mass storage device108, may, in appropriate cases, be incorporated in standard fashion aspart of primary storage 106 as virtual memory. A specific mass storagedevice such as a CD-ROM 114 may also pass data uni-directionally to theCPU.

CPU 102 is also coupled to an interface 110 that includes one or moreinput/output devices such as such as video monitors, track balls, mice,keyboards, microphones, touch-sensitive displays, transducer cardreaders, magnetic or paper tape readers, tablets, styluses, voice orhandwriting recognizers, or other well-known input devices such as, ofcourse, other computers. Finally, CPU 102 optionally may be coupled to acomputer or telecommunications network using a network connection asshown generally at 112. With such a network connection, it iscontemplated that the CPU might receive information from the network, ormight output information to the network in the course of performing theabove-described method steps. The above-described devices and materialswill be familiar to those of skill in the computer hardware and softwarearts.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. For example, the video image input may come from a widevariety of sources. Also, measurements may be taken of a variety offeatures at the micron level that are present on a variety of media, andnot necessarily a photomask. A polynomial calibration curve may be usedwith a variety of techniques for calculating a light measured value fora dimension of a feature. Therefore, the described embodiments should betaken as illustrative and not restrictive, and the invention should notbe limited to the details given herein but should be defined by thefollowing claims and their full scope of equivalents.

I claim:
 1. A computer-implemented method of determining the amount oflight flux from a microscopic defect located on a medium to assist withperforming a measurement of a dimension of said defect, said methodcomprising:receiving an image of said defect to be measured; producing aplurality of system regions of interest surrounding said defect to bemeasured; developing a light intensity distribution profile for each ofsaid system regions of interest; determining a total light fluxmeasurement for each of said light intensity distribution profiles; andchoosing one of said total light flux measurements as the bestmeasurement of said amount of light flux from said microscopic defect.2. A method as recited in claim 1 wherein said image is received from aproduction inspection machine and wherein said method is performed withsaid medium in place in said production inspection machine.
 3. A methodas recited in claim 1 wherein said step of choosing one of said totallight flux measurements as the best measurement includes the sub-stepof:calculating a standard deviation for a baseline of each of saidprofiles.
 4. A method as recited in claim 1 wherein said plurality ofsystem regions of interest includes a vertical region, a horizontalregion, a first region angled at 45 degrees from said vertical region,and a second region angled at minus 45 degrees from said verticalregion.
 5. A method as recited in claim 1 wherein each of said pluralityof system regions of interest surrounding said defect to be measured hasa size based at least in part upon a defect type of said feature.
 6. Amethod as recited in claim 1 wherein said step of determining a totallight flux measurement includes the sub-step of:determining a baselinefor each of said profile and subtracting said baseline from the profilefrom which it was determined.
 7. A method as recited in claim 1 whereinsaid dimension to be measured is less than about twice the wavelengthused for said measurement.
 8. A computer-implemented method ofdetermining the amount of light flux from a microscopic line located ona medium to assist with performing a measurement of a width of saidline, said method comprising:receiving an image of said line to bemeasured; producing a system region of interest surrounding a portion ofsaid line to be measured; determining a total light flux measurementassociated with said system region of interest, whereby said total lightflux measurement is useful in measuring the width of said line.
 9. Amethod as recited in claim 8 wherein said image is received from aproduction inspection machine and wherein said method is performed withsaid medium in place in said production inspection machine.
 10. A methodas recited in claim 8 wherein said step of producing produces aplurality of system regions of interest surrounding said portion of saidline to be measured, and wherein said plurality of system regions ofinterest include two parallel regions, whereby an angle of said line maybe compensated for.
 11. A method as recited in claim 10 furthercomprising:computing a rotation correction for said line based upon saidtwo parallel regions; and multiplying said rotation correction by saiddetermined light flux to compensate for an angle of said line.
 12. Amethod as recited in claim 8 wherein said step of determining a lightflux measurement includes the sub-steps of:developing a light intensitydistribution profile for said system region of interest; and determininga baseline for said profile and subtracting said baseline from saidprofile.
 13. A method as recited in claim 8 furthercomprising:calculating an area for a portion of said line using saiddetermined light flux; and determining a line width of said line bydividing said calculated area by a dimension of said system region ofinterest.
 14. A method as recited in claim 8 furthercomprising:developing a reference image corresponding to said systemregion of interest for assisting in determining said total light fluxmeasurement.
 15. A method as recited in claim 8 wherein said width to bemeasured is less than about twice the wavelength used for saidmeasurement.
 16. A method as recited in claim 8 furthercomprising:determining a line width of said line using said determinedlight flux measurement.
 17. A computer-implemented method of measuringthe line width of a microscopic line located on a mediumcomprising:receiving an image of said line to be measured; producing asystem region of interest surrounding said line to be measured;developing a light intensity distribution profile for said system regionof interest surrounding said line to be measured; determining a totallight flux measurement for said light intensity distribution profile;determining said line width of said line using said determined totallight flux measurement.
 18. A method as recited in claim 17 furthercomprising the steps of:producing two system regions of interestsurrounding said line; averaging a total light flux measurement for eachregion to obtain said total light flux measurement; and computing arotation correction based upon said two system regions of interest toadjust said total light flux measurement.
 19. A method as recited inclaim 17 wherein said step of determining a total light flux measurementincludes the sub-steps of:determining a baseline for said lightintensity distribution profile; and subtracting said baseline from saidprofile.
 20. A method as recited in claim 17 wherein said image isreceived from a production inspection machine and wherein said method isperformed with said medium in place in said production inspectionmachine.
 21. A method as recited in claim 17 wherein said line width tobe measured is less than about twice the wavelength used for saidmeasurement.
 22. A computer-implemented method of measuring a dimensionof a feature located on a medium, said method comprising:receiving animage of said feature to be measured, the dimension of said feature tobe measured having a size of less than about twice the wavelength usedfor said measurement; determining a flux value corresponding to saidfeature; determining said dimension of said feature using said fluxvalue.
 23. A method as recited in claim 22 further comprising:developingan intensity profile of said feature to be measured, said profile usefulfor determining said flux value.
 24. A method as recited in claim 22further comprising:correcting for said dimension by referring to anon-linear calibration curve.